mindspore.nn.probability.distribution.half_normal 源代码

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"""HalfNormal Distribution"""
from __future__ import absolute_import
from __future__ import division
import numpy as np
from mindspore import ops
from mindspore.ops import operations as P
from mindspore import _checkparam as Validator
from mindspore.common import dtype as mstype
from mindspore.nn.probability.distribution import Distribution
from mindspore.nn.probability.distribution._utils.utils import check_greater_zero


[文档]class HalfNormal(Distribution): r""" HalfNormal distribution. A HalfNormal distribution is a continuous distribution with the range :math:`[\mu, \inf)` and the probability density function: .. math:: f(x, \mu, \sigma) = 1 / \sigma\sqrt{2\pi} \exp(-(x - \mu)^2 / 2\sigma^2). where :math:`\mu, \sigma` are the mean and the standard deviation of the half normal distribution respectively. Args: mean (Union[int, float, list, numpy.ndarray, Tensor], optional): The mean of the distribution. If this arg is None, then the mean of the distribution will be passed in runtime. Default: None. sd (Union[int, float, list, numpy.ndarray, Tensor], optional): The standard deviation of the distribution. If this arg is None, then the sd of the distribution will be passed in runtime. Default: None. seed (int, optional): The seed used in sampling. The global seed is used if it is None. Default: None. dtype (mindspore.dtype, optional): The type of the event samples. Default: mstype.float32. name (str, optional): The name of the distribution. Default: 'HalfNormal'. Note: - `sd` must be greater than zero. - `dtype` must be a float type because HalfNormal distributions are continuous. - If the arg `mean` or `sd` is passed in runtime, then it will be used as the parameter value. Otherwise, the value passed in the constructor will be used. Raises: ValueError: When sd <= 0. TypeError: When the input `dtype` is not a subclass of float. Supported Platforms: ``CPU`` Examples: >>> import mindspore >>> import mindspore.nn as nn >>> from mindspore.nn.probability.distribution import HalfNormal >>> from mindspore import Tensor >>> # To initialize a HalfNormal distribution of the mean 3.0 and the standard deviation 4.0. >>> n1 = HalfNormal(3.0, 4.0, dtype=mindspore.float32) >>> # A HalfNormal distribution can be initialized without arguments. >>> # In this case, `mean` and `sd` must be passed in through arguments. >>> hn = HalfNormal(dtype=mindspore.float32) >>> # Here are some tensors used below for testing >>> value = Tensor([1.0, 2.0, 3.0], dtype=mindspore.float32) >>> mean_a = Tensor([2.0], dtype=mindspore.float32) >>> sd_a = Tensor([2.0, 2.0, 2.0], dtype=mindspore.float32) >>> mean_b = Tensor([1.0], dtype=mindspore.float32) >>> sd_b = Tensor([1.0, 1.5, 2.5], dtype=mindspore.float32) >>> ans = n1.log_prob(value) >>> print(ans.shape) (3,) >>> # Evaluate with respect to the distribution b. >>> ans = n1.log_prob(value, mean_b, sd_b) >>> print(ans.shape) (3,) >>> # `mean` and `sd` must be passed in during function calls >>> ans = hn.log_prob(value, mean_a, sd_a) >>> print(ans.shape) (3,) """ def __init__(self, mean=None, sd=None, seed=None, dtype=mstype.float32, name="HalfNormal"): """ Constructor of HalfNormal. """ param = dict(locals()) param['param_dict'] = {'mean': mean, 'sd': sd} valid_dtype = mstype.float_type Validator.check_type_name("dtype", dtype, valid_dtype, type(self).__name__) super(HalfNormal, self).__init__(seed, dtype, name, param) self._mean_value = self._add_parameter(mean, 'mean') self._sd_value = self._add_parameter(sd, 'sd') if self._sd_value is not None: check_greater_zero(self._sd_value, "Standard deviation") self.exp = P.Exp() self.cast = P.Cast() self.const = ops.scalar_to_tensor(np.sqrt(2. / np.pi)) self.sq = P.Square() self.type = dtype def _prob(self, value, mean=None, sd=None): r""" Evaluate probability of the value of the HalfNormal distribution. Args: value (Tensor): The value to be evaluated. mean (Tensor, optional): The mean of the distribution. Default: self._mean_value. sd (Tensor, optional): The standard deviation the distribution. Default: self._sd_value. .. math:: P(x) = 1 / \sigma \sqrt{2\pi} \exp(-(x - \mu)^2 / 2\sigma^2) """ value = self._check_value(value, 'value') value = self.cast(value, self.dtype) mean, sd = self._check_param_type(mean, sd) coeff = self.const / sd pdf = coeff * self.exp(-0.5 * self.sq((value - mean) / sd)) return pdf * self.cast(value >= 0, self.type)